Summary: This blog explores the types of AI in healthcare and how they help staff improve efficiency, reduce burnout, and enhance patient care.
- Rule-based systems for clinical decision support
- Machine learning for diagnostics and planning
- Deep learning for imaging and EHR analysis
- NLP for documentation and coding
- RPA for administrative automation
- Predictive analytics for utilization management
- Conversational AI for patient interaction
We have officially entered the age of AI. Today, machine learning offers healthcare systems effortless access to practical, data-driven solutions. Artificial intelligence has the power to streamline operations and improve the quality of care.
Understanding the types of AI in healthcare is the first step toward achieving better outcomes and reduced burnout. If artificial intelligence isn’t currently driving your solutions, it’s time to catch up and discover the benefits of AI in healthcare.
In this blog, we’ll discuss the major applications of AI in healthcare. Continue reading to learn about the solutions that can optimize your existing workflows in the healthcare setting and beyond.
1. Rule-Based Systems (Expert Systems)
Rule-based systems use “if-then” logic to support clinical decision-making. These systems are often integrated into clinical decision support tools. They are used to help flag contraindications, drug interactions, or missing documentation. Rule-based systems are capable of easing the cognitive load for physicians while reducing medical errors.
Examples of artificial intelligence in healthcare featuring expert systems include clinical alerts in EHRs and protocol adherence systems.
2. Machine Learning (ML)
Machine Learning algorithms use historical data to guide decisions without being explicitly programmed. In healthcare, ML powers risk stratification models, diagnostic support tools, and personalized treatment plans. These tools can improve early detection and diagnosis, and even enhance population health management.
3. Deep Learning in Healthcare
A subset of ML, deep learning in healthcare involves neural networks capable of processing unstructured data. For example, deep learning can process visual patient data like medical images and pathology slides.
The strength of deep learning tools lies in their ability to uncover complex patterns that traditional analytics often miss. By analyzing imaging data at scale, Deep Learning supports faster, more accurate diagnoses. At the same time, this technology can reduce the need for time-consuming manual chart reviews.
4. Natural Language Processing (NLP)
NLP enables computers to understand, interpret, and generate human language. It’s the same technology used by smart assistants and email filters. It’s even used as part of the autocorrect and predictive text tools we use every day.
In healthcare, NLP is used to extract insights from unstructured clinical text. It can summarize documents such as discharge summaries, nursing notes, physician dictation, and more. As a result, it speeds up documentation and coding workflows. Many NLP solutions for healthcare can even flag missed diagnoses or documentation gaps.
5. Robotic Process Automation (RPA)
Robotic Process Automation (RPA) is designed to handle repetitive, rule-based tasks. It works by mimicking human interactions with software. While not “intelligent” in the traditional sense, RPA boosts administrative efficiency in hospitals and clinics.
For example, RPA automates billing and claims processing, reducing administrative overhead and minimizing human error.
6. Predictive Analytics & Utilization Management
Predictive models can forecast patient outcomes and resource needs. In utilization management, AI-driven tools like those from Xsolis can analyze real-time patient data. The goal is to support case reviews, compliance, and optimal level-of-care decisions.
For example, the Dragonfly Utilize platform applies AI and machine learning to reduce friction between providers and payers. It has the power to enhance clinical efficiency and improve case review collaboration. This AI-driven approach transforms how staff engage with real-time data. Once implemented, it can reduce administrative burden and improve patient throughput.
7. Conversational AI & Virtual Assistants
Conversational AI tools like chatbots and voice assistants are increasingly found in patient-facing roles. These tools answer frequently asked questions, freeing up human staff to address more complex, higher-value tasks.
Additionally, these tools can handle appointment scheduling, and some can even assist with basic triage. These tools free up front-desk and clinical staff, permitting them to provide more compassionate, face-to-face care.
The Future of Artificial Intelligence Trends in Healthcare
The applications of AI in healthcare are expanding by the day. With trusted partners like Xsolis, you can implement AI in a way that aligns with your institutional goals.
See how Xsolis uses AI to transform case review and reduce clinician burden — request a demo today.